Search Results for author:
Found 14 papers, 9 papers with code
Can Shadows Reveal Biometric Information?
We transfer what we learn from the synthetic data to the real data using domain adaptation in a completely unsupervised way.
Gemino: Practical and Robust Neural Compression for Video Conferencing
We design Gemino, a new neural compression system for video conferencing based on a novel high-frequency-conditional super-resolution pipeline.
Differentiable Rendering of Neural SDFs through Reparameterization
Our method leverages the distance to surface encoded in an SDF and uses quadrature on sphere tracer points to compute this warping function.
Plug-and-Play Algorithms for Video Snapshot Compressive Imaging
On the other hand, applying SCI to large-scale problems (HD or UHD videos) in our daily life is still challenging and one of the bottlenecks lies in the reconstruction algorithm.
AsyncTaichi: On-the-fly Inter-kernel Optimizations for Imperative and Spatially Sparse Programming
These domain-specific optimizations further make way for classical general-purpose optimizations that are originally challenging to directly apply to computations with sparse data structures.
When and how CNNs generalize to out-of-distribution category-viewpoint combinations
In this paper, we investigate when and how such OOD generalization may be possible by evaluating CNNs trained to classify both object category and 3D viewpoint on OOD combinations, and identifying the neural mechanisms that facilitate such OOD generalization.
Painting Many Pasts: Synthesizing Time Lapse Videos of Paintings
We present a probabilistic model that, given a single image of a completed painting, recurrently synthesizes steps of the painting process.
DiffTaichi: Differentiable Programming for Physical Simulation
We present DiffTaichi, a new differentiable programming language tailored for building high-performance differentiable physical simulators.
Generating Training Data for Denoising Real RGB Images via Camera Pipeline Simulation
Unfortunately, the commonly used additive white noise (AWGN) models do not accurately reproduce the noise and the degradation encountered on these inputs.
Data augmentation using learned transformations for one-shot medical image segmentation
Image segmentation is an important task in many medical applications.
Ranked #1 on
Brain Image Segmentation
on T1-weighted MRI
Differentiable Monte Carlo Ray Tracing through Edge Sampling
We introduce a general-purpose differentiable ray tracer, which, to our knowledge, is the first comprehensive solution that is able to compute derivatives of scalar functions over a rendered image with respect to arbitrary scene parameters such as camera pose, scene geometry, materials, and lighting parameters.
Learning-based Video Motion Magnification
We show that the learned filters achieve high-quality results on real videos, with less ringing artifacts and better noise characteristics than previous methods.
Deep Bilateral Learning for Real-Time Image Enhancement
For this, we introduce a new neural network architecture inspired by bilateral grid processing and local affine color transforms.
What do different evaluation metrics tell us about saliency models?
How best to evaluate a saliency model's ability to predict where humans look in images is an open research question.
